benchmark-training-sync

To sync CLI options between benchmark-training and learn-model, run an automated validation process that identifies missing options and verifies the test exclusion list to ensure feature parity for benchmarking training performance.

Validating CLI command synchronization ensures the benchmark-training command maintains feature parity with learn-model, preventing option discrepancies and ensuring consistent training performance evaluation across your Python project.

Yes, you need pytest and click installed in your Python environment to execute compatibility tests and verify CLI option synchronization between the benchmark-training and learn-model commands.

To verify the exclusion list for intentionally omitted benchmark-training options, run the validator which checks the accuracy and justification of options excluded from the learn-model synchronization process.

You should run a CLI synchronization check after adding a new hyperparameter tuning option to learn-model, ensuring the same option is available and correctly implemented in benchmark-training for performance evaluation.